Most of the widest EBITDA-versus-revenue spreads in any five-year screen are fake. Not wrong, exactly: the arithmetic is correct. But when the base year is 2020, a company that saw earnings collapse and then recover looks identical to one that genuinely expanded its margins, and the first group is far larger than the second. This week's screen produced more than thirty positive spreads and rejected most of them on that single test, comparing each base year against the one before it to see whether the starting point was a normal year or a hole.
What survived are five businesses where the gap between EBITDA growth and revenue growth came from operating leverage rather than from the denominator. Built on FMP's Income Statement API across the S&P 500 and S&P MidCap 400, this screen walks through what each spread is actually made of.
Key Takeaways
- The quality gate mattered more than the ranking: spreads of 55, 40 and 38 percentage points were all removed for base-year distortion, spinoffs or acquisitions, while the survivors top out at 11.34.
- Fiscal-year alignment is not a detail. Several non-December filers initially screened on the wrong five-year window, and correcting it cut one candidate's spread from 13.5 percentage points to 6.31 and another's from 8.0 to 0.66.
- The five survivors reach the same result through different mechanisms: scale economics, a completed platform transition, acquisition integration, infrastructure absorption, and product mix.
- A widening gap between EBITDA and revenue growth is durable only while the source of the leverage is. Identifying which lever is doing the work is the analytical step the CAGR itself skips.
Five Businesses Where Margin Is Outrunning Revenue
Arista Networks (ANET)
5-Year Revenue CAGR: 31.19%
5-Year EBITDA CAGR: 42.53%
Arista compounded revenue at better than 31% a year over five years and still grew EBITDA more than eleven percentage points faster, which is the hardest version of this pattern to produce. Scaling that quickly usually costs margin: headcount, support infrastructure and supply chain all expand ahead of revenue. Arista's did not, and the 2026 results have extended it, with AI networking demand described as running above available supply and full-year guidance raised on the back of it.
The structural reason is that Arista sells a software-defined networking stack where incremental revenue carries a high gross margin and the operating cost base scales sub-linearly. That is a real economic property rather than a cost-control achievement. It comes with a specific vulnerability: the same hyperscale customers that produce the operating leverage also produce concentration, and a spread this wide depends on those relationships remaining intact and on pricing holding as competitors move into back-end AI networking.
Gross margin is the cleanest place to watch for pressure, since it moves before operating margin does. FMP's Financial Ratios API carries that series across the full five-year window, which is what shows whether the leverage is holding or whether mix and pricing are starting to erode it. Customer concentration disclosure, back-end versus front-end revenue split, and supply availability are the items that determine how long the pattern runs.
Veeva Systems (VEEV)
5-Year Revenue CAGR: 16.88%
5-Year EBITDA CAGR: 25.04%
Veeva's window runs from its January 2021 fiscal year to January 2026, and it is one of the few names in the screen whose base year was stronger than the one before it, which removes any question about a depressed starting point. Revenue compounded in the mid-teens while EBITDA compounded at 25%, a spread of just over eight percentage points achieved without the growth rate that carries Arista's.
What produced it was the completion of an expensive transition rather than a step change in the business. Veeva spent several years building and migrating customers onto its own clinical and commercial platform, and the fiscal 2026 results reflect a period where that investment has moved from cost to contribution. Layered on top is a set of AI applications sold into the same installed base, which is the cheapest possible revenue for a subscription business because the customer, the contract and the infrastructure already exist.
That makes returns on invested capital the more revealing metric than margin alone, because the question is whether the platform investment is earning back. FMP's Key Metrics API carries return on invested capital and the related efficiency series across the period. Subscription revenue growth relative to services, customer migration completion, and the attach rate on the newer applications are where the durability of the spread is decided.
CRH (CRH)
5-Year Revenue CAGR: 10.71%
5-Year EBITDA CAGR: 18.40%
CRH is the only building materials business in the screen and the one where the leverage comes from the least glamorous source: buying assets and running them better. Revenue compounded at just under 11% while EBITDA compounded at better than 18%, and the company has guided to 2026 adjusted EBITDA in the $8.1 billion to $8.5 billion range while continuing to expand, including a water infrastructure platform and an in-progress transaction carrying a stated synergy target.
Two caveats belong with this one. The first is that the FY2020 base sits about 14% below FY2019, close enough to the quality threshold to be worth naming: a portion of the spread reflects recovery rather than expansion. The second is that a serial acquirer's margin improvement is partly a portfolio effect, with lower-margin assets divested and higher-margin ones added, which is a real source of value but a different one from same-asset operating leverage. Both readings are defensible; they are not the same claim.
Enterprise value is the frame that keeps an acquisition-funded record honest, because EBITDA growth financed by capital deployment has to be judged against the capital. FMP's Enterprise Values API supplies the debt, cash and enterprise value series that make that comparison possible. Organic versus acquired growth, the synergy delivery against stated targets, and infrastructure backlog are what separate the two explanations.
Alphabet (GOOGL)
5-Year Revenue CAGR: 17.15%
5-Year EBITDA CAGR: 23.89%
Alphabet is the largest company in the screen by a wide margin, and the presence of a nearly seven percentage point spread at this scale is more notable than the number suggests. A business compounding revenue at 17% off a base this size is already unusual; doing it while expanding margin means the incremental revenue is arriving at a higher margin than the average, which points at mix rather than at cost discipline.
The mix in question is the shift in the revenue base over the window. Cloud moved from a loss-making segment to a profitable one during this period, which mechanically lifts consolidated EBITDA growth above revenue growth without anything changing in the advertising business. Meanwhile capital expenditure has risen sharply to fund AI infrastructure, which pressures free cash flow while leaving EBITDA largely untouched, since depreciation sits below the line. The spread is real and it is also, in part, an artifact of measuring at the EBITDA line rather than further down.
Segment reporting is therefore essential rather than optional here, and FMP's Revenue Product Segmentation API breaks the top line into its constituent businesses so the mix effect can be isolated from the operating one. Cloud operating margin, the capital expenditure trajectory, and the depreciation charge flowing through to net income are what show whether the EBITDA spread survives translation into cash.
Hubbell (HUBB)
5-Year Revenue CAGR: 9.68%
5-Year EBITDA CAGR: 16.40%
Hubbell produced the narrowest spread among the five and arguably the most straightforward story. Revenue compounded near 10% while EBITDA compounded above 16%, and the 2026 results have continued in the same direction, with guidance raised on demand from utility grid investment and electrification. The FY2020 base sits only marginally below FY2019, so the starting point is close to clean.
The mechanism is capacity absorption. Hubbell makes electrical and utility infrastructure products in plants with meaningful fixed costs, and a sustained increase in volume spreads those costs across more units. That produces exactly this pattern, and it is genuine, but it is also the most cycle-dependent form of operating leverage in the group. Utility capital spending is currently in an unusually strong phase driven by grid replacement and load growth; the same fixed-cost base that amplifies margin on the way up amplifies it on the way down.
Watching the growth series rather than the levels is what makes the turn visible, since operating leverage reverses before revenue does. FMP's Financial Statement Growth API carries revenue, EBITDA and margin growth in comparable form across the period. Utility segment orders, price realisation against input costs, and capacity utilisation are the disclosures that would signal the cycle changing phase.
Why the Gap Matters More Than the Growth Rate
The reject list from this screen is more useful than the results. One company showed a 55 percentage point spread and failed because its base year predated a cost structure that no longer exists. Several others showed spreads between 30 and 40 points that were entirely the product of a 2020 collapse in hotels, restaurants, retail or travel, followed by a normal recovery. A separate group failed because a spinoff or a large acquisition meant the base year and the end year describe different companies. None of those results is a calculation error. They are all correct arithmetic applied to a comparison that does not hold.
Fiscal-year alignment caused a subtler version of the same problem. Companies with January, June or September year ends were initially measured on a window that ended one fiscal year short of their latest report, which flattered some and penalised others. Correcting to a consistent latest-minus-five basis cut one candidate from 13.5 percentage points to 6.31, and another from 8.0 to 0.66, which is the difference between a headline and nothing at all. In a screen sorted by spread, an alignment error does not produce a small inaccuracy. It produces the wrong five companies.
Once the survivors are real, the question becomes which lever created the spread, because the levers have very different lifespans. Arista's comes from software economics at scale, which persists while pricing and customer relationships do. Veeva's comes from the far side of a platform transition, which is a one-time improvement that resets to a new baseline rather than compounding indefinitely. CRH's comes partly from portfolio construction. Alphabet's comes substantially from one segment crossing into profitability. Hubbell's comes from fixed-cost absorption during a strong capital spending cycle, which reverses symmetrically. Five similar numbers, five different half-lives.
Testing those explanations takes the statements together rather than one at a time. The Income Statement Bulk API is what makes the screen practical across a universe rather than a watchlist, and the Income Statement API supplies the seven-year history needed to check the base year against the one before it, which is the step that removed most of this week's candidates. From there the Cash Flow Statement API answers whether EBITDA growth is converting, which is where Alphabet's capital expenditure and Hubbell's working capital both matter. The Balance Sheet Statement API shows whether the growth was funded or earned, which is the central question for CRH. The Key Metrics TTM API and the Financial Scores API together normalise across five businesses that share no comparable operating model, and the Enterprise Values API keeps an acquisitive record measured against the capital behind it. Running that sequence across one consistent dataset is the reason the breadth available through the FMP platform matters here more than in most screens: a spread computed from one endpoint and audited from five others is a different object from a spread computed alone.
The broader point is that a five-year CAGR comparison is a detector, not a verdict. It reliably finds companies where something changed in the relationship between revenue and profit. It cannot distinguish between a structural improvement, a cyclical one, a portfolio effect and a measurement artifact, and in an unfiltered ranking the artifacts win. The value of the screen is in what it surfaces for examination, and the examination is the work.
Building a Consistent CAGR Screening Framework
Building a useful CAGR screen is less about the formula itself and more about maintaining discipline in the underlying dataset. The calculation is straightforward; what determines whether the result is meaningful is data consistency. Every company needs to be evaluated using the same reporting periods, identical financial line items, and the same time horizon. Once those inputs are standardized, growth rates become comparable across industries, capital structures, and business models. The workflow below shows how to structure that process using FMP's Income Statement data — starting with a single company and then scaling the exact same logic across a broader universe.
Step 1: Pull Income Statement Data
Begin with a single symbol to establish the baseline. Query the standard Income Statement API to retrieve the full set of historical reporting periods needed for the calculation.
As long as your API key is active, one request gives you the raw time series you'll be working with. For example:
Endpoint:
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https://financialmodelingprep.com/stable/income-statement?symbol=AAPL&apikey=YOUR_API_KEY |
Step 2: Gather Historical Figures
From the JSON output, select the specific metric you want to analyze — revenue, EBITDA, EPS, or another line item. Arrange the values in proper chronological order before doing any math. This step is easy to overlook, but it's critical: CAGR only makes sense when the starting and ending points are clearly defined and consistently ordered.
Step 3: Calculate CAGR
Once the first and last data points are set, calculate CAGR using the standard formula:
CAGR = (Ending Value / Beginning Value)^(1 / Years) - 1
This reduces several years of performance into a single annualized figure, making it easier to compare growth profiles across companies without getting lost in interim volatility.
Step 4: Scale Screening with Bulk API
After validating the method on one symbol, broaden the workflow using the Income Statement Bulk API:
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https://financialmodelingprep.com/stable/income-statement-bulk?year=2025&period=FY&apikey=YOUR_API_KEY |
Running the same calculation at scale lets you build filters — for instance, highlighting companies that clear a five-year revenue CAGR threshold — while ensuring every ticker is processed under the same ruleset. Once the bulk pull is in place, updating or rerunning the screen is effectively a single action.
Scaling the Framework Without Changing the Methodology
The strength of this type of screen comes from consistency, not complexity. Once the formula, reporting periods, and financial line items are defined, the objective is to keep the methodology fixed while gradually widening the universe being tested. Expanding coverage should not require changing the framework itself — only the number of companies moving through it.
That's why the workflow is easiest to validate in a smaller environment first. Within the Basic plan, the Income Statement endpoints provide enough historical coverage to align reporting periods properly, normalize the selected metrics, and verify that the CAGR calculations are producing comparable outputs across companies. At this stage, the emphasis is less about scale and more about making sure inconsistencies in filings or missing data are not distorting the screen.
From there, expanding into the Starter plan simply broadens the sample size. The screening logic remains identical, but the larger universe makes sector-level comparisons more useful. Patterns that initially appear company-specific can then be evaluated against peers, industries, or market-cap cohorts to determine whether the operating leverage signal is isolated or part of a broader trend developing within a segment of the market.
The Premium plan extends that same structure further by increasing historical depth and geographic coverage. The underlying process still does not change. What changes is the scope of observation — allowing the same framework to be applied across wider datasets without introducing new assumptions or altering the screening criteria midstream. That continuity is what makes the process repeatable over time rather than dependent on one-off observations or isolated market conditions.
Turning a Five-Year Read Into a Running One
Five years is long enough to strip out a bad quarter and short enough that a single distorted base year can carry the whole result, which is why the window has to be checked as carefully as the calculation. Rebuilt on a schedule from the Income Statement API and Income Statement Bulk API, the screen rolls its own base year forward and the distortions age out of it on their own.
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Disclosure: Signals Desk content is provided for informational and analytical purposes only and does not constitute investment advice or trade recommendations. The analysis reflects interpretation of market data and publicly disclosed or third-party information, including data accessed via Financial Modeling Prep APIs, at the time of publication. Signals discussed are probabilistic, can be wrong, and may change as market conditions and consensus data evolve. This content should be considered alongside broader research, individual objectives, and risk assessment.
